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ABSTRACT: The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.
Neural Computation 03/2013; · 1.88 Impact Factor
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ABSTRACT: We use a supervised multi-spike learning algorithm for spiking neural networks (SNNs) with temporal encoding to simulate the learning mechanism of biological neurons in which the SNN output spike trains are encoded by firing times. We first analyze why existing gradient-descent-based learning methods for SNNs have difficulty in achieving multi-spike learning. We then propose a new multi-spike learning method for SNNs based on gradient descent that solves the problems of error function construction and interference among multiple output spikes during learning. The method could be widely applied to single spiking neurons to learn desired output spike trains and to multilayer SNNs to solve classification problems. By overcoming learning interference among multiple spikes, our method has high learning accuracy when there are a relatively large number of output spikes in need of learning. We also develop an output encoding strategy with respect to multiple spikes for classification problems. This effectively improves the classification accuracy of multi-spike learning compared to that of single-spike learning.
Neural networks: the official journal of the International Neural Network Society 02/2013; 43C:99-113. · 1.88 Impact Factor
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ABSTRACT: Our study first proposed that curcumin could protect human endothelial cells from the damage caused by oxidative stress via autophagy. Furthermore, our results revealed that curcumin causes some novel cellular mechanisms that promote autophagy as a protective effect. Pretreatment with curcumin remarkably improves the survival of human umbilical vein endothelial cells (HUVECs) from H 2O 2-induced viability loss, which specifically evokes an autophagic response. Exposed to H 2O 2, curcumin-treated HUVECs upregulate the level of microtubule-associated protein 1 light chain 3-II (LC3-II), the number of autophagosomes, and the degradation of p62. We show that this compound promotes BECN1 expression and inhibits the phosphatidylinositol 3-kinase (PtdIns3K)-AKT-mechanistic target of rapamycin (MTOR) signaling pathway. Curcumin can also reverse FOXO1 (a mediator of autophagy) nuclear localization along with causing an elevated level of cytoplasmic acetylation of FOXO1 and the interaction of acetylated FOXO1 and ATG7, under the circumstance of oxidative stress. Additionally, knockdown of FOXO1 by shRNA inhibits not only the protective effects that curcumin induced, but the autophagic process, from the quantity of LC3-II to the expression of RAB7. These results suggest that curcumin induces autophagy, indicating that curcumin has the potential for use as an autophagic-related antioxidant for prevention and treatment of oxidative stress. These data uncover a brand new protective mechanism involving FOXO1 as having a critical role in regulating autophagy in HUVECs, and suggest a novel role for curcumin in inducing a beneficial form of autophagy in HUVECs, which may be a potential multitargeted therapeutic avenue for the treatment of oxidative stress-related cardiovascular diseases.
Autophagy 05/2012; 8(5):812-25. · 7.45 Impact Factor
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JSW. 01/2012; 7:149-155.
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IEEE Transactions on Multimedia. 01/2012; 14:111-120.
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SIAM J. Numerical Analysis. 01/2012; 50:79-104.
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CSCW '12 Computer Supported Cooperative Work, Seattle, WA, USA, February 11-15, 2012; 01/2012
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CSCW '12 Computer Supported Cooperative Work, Seattle, WA, USA, February 11-15, 2012; 01/2012
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Int. J. Digital Earth. 01/2011; 4:305-329.
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UbiComp 2011: Ubiquitous Computing, 13th International Conference, UbiComp 2011, Beijing, China, September 17-21, 2011, Proceedings; 01/2011
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J. Database Manag. 01/2011; 22:26-42.
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Educational Technology & Society. 01/2011; 14:181-191.
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Eighth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 26-28 July 2011, Shanghai, China; 01/2011
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Multimodal Brain Image Analysis, First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings; 01/2011
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4th International Symposium on Computational Intelligence and Design, ISCID 2011, Hangzhou, China, October 28-30, 2011, 2 Volumes; 01/2011
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Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011
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Proceedings of the International Conference on Information Systems, ICIS 2011, Shanghai, China, December 4-7, 2011; 01/2011
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Knowledge Science, Engineering and Management - 5th International Conference, KSEM 2011, Irvine, CA, USA, December 12-14, 2011. Proceedings; 01/2011
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J. Computational Applied Mathematics. 01/2011; 236:988-995.
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JSW. 01/2011; 6:116-123.
